Prediction Of Contact Forces Research Articles

Effects of loading processes on contact forces when simulating static seating with a finite element human body model

Seat interface forces, particularly shear forces, play an essential role in predicting the risk of pressure ulcers and seating discomfort. When a finite element human body model (HBM) is used for static seating simulation, the most common loading method is to put the model in a position close to the desired final posture and then ‘drop’ it from just above the seat by applying the gravity (DROP). This does not represent how people sit in a seat. In addition, high coefficients of friction (COF) are often used to prevent sliding, which may lead to unrealistically high tangential forces. This study aims to investigate the effects of the loading process and the COF on seating simulations with a HBM. We propose a new loading approach called ‘drop and rotate’ (D&R) to better mimic people sitting on a seat. With the trunk more flexed than the desired posture, the model is dropped to establish the contact between the buttocks and thighs, and the seat pan first, and then between the back and the backrest by rotating the hip. Simulations were performed using a recently developed and validated adult male model in two different seat configurations. Results show that the proposed D&R method was less sensitive to COF and gave a better prediction of contact forces, especially on the seat pan. However, its computational time is higher than the DROP method. The study highlights the importance of the loading process when simulating static seating.

Safety-Centric Precision Control of a Modified Duodenoscope Designed for Surgical Robotics

There is limited research on robotic systems designed for Endoscopic Retrograde Cholangiopancreatography (ERCP) procedures using a side-view duodenoscope. The unique structure of the duodenoscope presents challenges to safely and precisely control the distal end pose. Control methods applied can reduce potential medical risks. We have redesigned the control section of the duodenoscope to facilitate its manipulation by a robotic system. An orthogonal compensator is employed to rectify the motion planes to standard planes. A hysteresis compensator based on the Prandtl-Ishlinskii model enables precise control of the distal pose of the duodenoscope. Furthermore, we utilize a contact force prediction model to prevent excessive contact force at the distal end. The performance of the modified duodenoscope is comparable to that of the standard duodenoscope. Following orthogonal compensation, the deviation angles of the motion planes is reduced by 32% to 98%. Post-hysteresis compensation, the root mean square error (RMSE) of the output angle of the distal end is decreased from 8.347° to 4.826°. The accuracy of distal end contact force prediction was approximately ±25% under conditions of high contact force. In conclusion, the modification and control strategy we proposed can achieve relatively safe and precise control of bending section, laying the foundation for the subsequent roboticization of duodenoscope systems for ERCP procedures.

Analysis of deformation and multi-angle tip contact in a combined mechanism of thin-walled membrane forces and inextensible layers in soft actuators

Soft actuators have extensive applications in operations, are generally made of elastomers, and generate large deformation. It makes accurately predicting their deformation behavior challenging. This study considers the combined effects of chamber sidewall expansion and inextensible layer strain mechanisms on the actuator's bending and tip contact force. We separately model the expansion of the chamber sidewall and the strain in the inextensible layer using the expanding membrane and strain beam theory. The theoretical model presented to predict the contact area of the sidewall expanding membrane under multiple bending conditions. The analytical models for three actuator states are established: 1) Free space; 2) Tip contact at multiple angles; and 3) Grasping state. The proposed theoretical model accurately predicts deformation, force characteristics, and the requisite pressure for object grasping, and its applicability extends to similar soft actuators. Comparative analyses with finite element methods (FEM) and experimental results validate the model's computational efficiency, reduced design variables, and accurate prediction of diverse deformations and tip contact forces. Notably, the model outperforms with shorter computation times. The large and small chamber spacing errors are approximately 10 % and 5 %, respectively.

On the prediction of tibiofemoral contact forces for healthy individuals and osteoarthritis patients during gait: a comparative study of regression methods.

Knee osteoarthritis (OA) is a public health problem affecting millions of people worldwide. The intensity of the tibiofemoral contact forces is related to cartilage degeneration, and so is the importance of quantifying joint loads during daily activities. Although simulation with musculoskeletal models has been used to calculate joint loads, it demands high-cost equipment and a very time-consuming process. This study aimed to evaluate consolidated machine learning algorithms to predict tibiofemoral forces during gait analysis of healthy individuals and knee OA patients. Also, we evaluated three different datasets to train each model, considering different combinations of primary kinematic and kinetic data, and post-processing data. We evaluated 14 patients with severe unilateral knee OA and 14 healthy individuals during 3-5 gait trials. Data were split into 70% and 30% of the samples as training and test data. Test data was independently evaluated considering a mixture of pathological and healthy individuals, and only OA and Control patients. The main results showed that accurate predictions of the tibiofemoral contact forces were achieved using machine learning methods and that the predictions were sensitive to changes in the input data as training. The present study provided insights into the most promising regressions methods to predict knee contact forces representing an important starting point for the broader application of biomechanical analysis in clinical environments.

Prediction of medial knee contact force using multisource fusion recurrent neural network and transfer learning.

Estimation of knee contact force (KCF) during gait provides essential information to evaluate knee joint function. Machine learning has been employed to estimate KCF because of the advantages of low computational cost and real-time. However, the existing machine learning models do not adequately consider gait-related data's temporal-dependent, multidimensional, and highly heterogeneous nature. This study is aimed at developing a multisource fusion recurrent neural network to predict the medial condyle KCF. First, a multisource fusion long short-term memory (MF-LSTM) model was established. Then, we developed a transfer learning strategy based on the MF-LSTM model for subject-specific medial KCF prediction. Four subjects with instrumented tibial prostheses were obtained from the literature. The results showed that the MF-LSTM model could predict medial KCF to a certain high level of accuracy (the mean of ρ = 0.970). The transfer learning model improved the prediction accuracy (the mean of ρ = 0.987). This study shows that the MF-LSTM model is a powerful and accurate computational tool for medial KCF prediction. Introducing transfer learning techniques could further improve the prediction performance for the target subject. This coupling strategy can help clinicians accurately estimate and track joint contact forces in real time.

Multi-Action Knee Contact Force Prediction by Domain Adaptation.

Most recent musculoskeletal dynamics estimation methods are designed for predefined actions, such as gait, and don't generalize to various tasks. In this work, we address the problem of estimating internal biomechanical forces during more than one actions by introducing unsupervised domain adaptation into a deep learning model. More specifically, we developed a Bidirectional Long Short-Term Memory network for knee contact force prediction, enhanced with correlation alignment layers, in order to minimize the domain shift between kinematic data from different actions. Furthermore, we used the novel Neural State Machine (NSM) as a simulation platform to test and visualize our model predictions in a wide range of trajectories adapted to different 3D scene geometries in real-time. We conducted multiple experiments, including comparison with previous models, model alignment across action classes and real-to-synthetic data alignment. The results showed that the proposed deep learning architecture with domain adaptation performs better than the benchmark in terms of NRMSE and t-test. Overall, our method is capable of predicting knee contact forces for more than one action classes using a single architecture and thereby opens the path for estimating internal forces for intermediate actions, while the knowledge of the hidden state of motion may be used to support personalized rehabilitation. Moreover, our model can be easily integrated into any human motion simulation environment, which shows its potential in enabling biomechanical analysis in an automated and computationally efficient way.

Comparative validation of two patient-specific modelling pipelines for predicting knee joint forces during level walking

Over the past few years, the use of computer models and simulations tailored to the patient's physiology to assist clinical decision-making has increased enormously.While several pipelines to develop personalized models exist, their adoption on a large scale is still limited due to the required niche computational skillset and the lengthy operations required. Novel toolboxes, such as STAPLE, promise to streamline and expedite the development of image-based skeletal lower limb models. STAPLE-generated models can be rapidly generated, with minimal user input, and present similar joint kinematics and kinetics compared to models developed employing the established INSIGNEO pipeline. Yet, it is unclear how much the observed discrepancies scale up and affect joint contact force predictions. In this study, we compared image-based musculoskeletal models developed (i) with the INSIGNEO pipeline and (ii) with a semi-automated pipeline that combines STAPLE and nmsBuilder, and assessed their accuracy against experimental implant data.Our results showed that both pipelines predicted similar total knee joint contact forces between one another in terms of profiles and average values, characterized by a moderately high level of agreement with the experimental data. Nonetheless, the Student t-test revealed statistically significant differences between both pipelines. Of note, the STAPLE-based pipeline required considerably less time than the INSIGNEO pipeline to generate a musculoskeletal model (i.e., 60 vs 160 min). This is likely to open up opportunities for the use of personalized musculoskeletal models in clinical practice, where time is of the essence.

Robot-Assisted Teleoperation Ultrasound System Based on Fusion of Augmented Reality and Predictive Force

This study presents a novel method to realize an augmented reality (AR) and haptic feedback teleoperation ultrasound system, which can reduce the influence of time delay on the operator. The physician uses a haptic device to send commands to a remote site while observing the AR environment. In the virtual environment, the organ model is projected onto the corresponding position according to the human pose, and the UR robot model can respond to the physician's action in advance. Based on force data, the dynamic environmental model is updated by the recursive least-squares method, allowing real-time force feedback. The contact force is computed from the identified parameters together with the position and velocity of the master site. An experimental platform has been built, where experiments have been conducted to evaluate the overall architecture, highlighting the role of AR and contact-force prediction models, and including clinical validation. Results show that the system can correctly perform remote ultrasound scanning tasks and ensure authenticity and synchronization.

Propulsion kinetics of recumbent handcycling during high and moderate intensity exercise

People with spinal cord injuries (PwSCI) are at high risk of developing cardiovascular disease (CVD). While regular exercise can reduce risk of CVD, PwSCI face various barriers to exercise, including high rates of upper limb injuries, especially in the shoulder. Handcycling high intensity interval training (HIIT), which consists of periods of high intensity exercise followed by rest, is a potential exercise solution, but the musculoskeletal safety of HIIT is still unknown. In this study, we characterized three-dimensional continuous applied forces at the handle during handcycling HIIT and moderate intensity continuous training (MICT). These applied forces can give an early indication of joint loading, and therefore injury risk, at the shoulder. In all three directions (tangential, radial, and lateral), the maximum applied forces during HIIT were larger than those in MICT at all timepoints, which may indicate higher contact forces and loads on the shoulder during HIIT compared to MICT. The tangential and radial forces peaked twice in a propulsion cycle, while the lateral forces peaked once. Throughout the exercises, the location of tangential peak 2 and radial peak 1 was later in HIIT compared to MICT. This difference in maximum force location could indicate either altered kinematics or muscular fatigue at the end of the exercise protocol. These changes in kinematics should be more closely examined using motion capture or other modeling techniques. If we combine this kinetic data with kinematic data during propulsion, we can create musculoskeletal models that more accurately predict contact forces and injury risk during handcycling HIIT in PwSCI.

Multibody Dynamics of Cross Groove Constant Velocity Ball Joints for High Performance Racing Applications

Accurate prediction of constant velocity joint transient contact loading and complex kinematics is necessary to improve joint design and prevent incurring failure from rolling contact fatigue. A detailed multibody dynamic model is presented for cross groove constant velocity joints used in high performance automotive racing applications, hitherto not reported in the open literature. The developed model includes detailed contact mechanics and friction models for all contacts made between the components. A novel semi-Hertzian contact model is adopted for the cage-race conjunction. Local contact kinematics is evaluated based on a methodology used in tribological analysis. The proposed model is validated against available computational models provided in literature for cross groove designs. Using the model, contact pressures occurring in the ball-race and ball-cage contacts are shown to reach levels as high as 4 and 5 GPa, respectively. The assumed value of friction coefficient is shown to have a strong influence on the predicted contact forces. Local contact motions are presented for the ball-race and ball-cage contacts, demonstrating complex motion which varies between pure rolling and pure sliding within a single cycle.

Can a Musculoskeletal Model Adapted to Knee Implant Geometry Improve Prediction of 3D Contact Forces and Moments?

Tibiofemoral contact loads are crucial parameters in the onset and progression of osteoarthrosis. While contact loads are frequently estimated from musculoskeletal models, their customization is often limited to scaling musculoskeletal geometry or adapting muscle lines. Moreover, studies have usually focused on superior-inferior contact force without investigating three-dimensional contact loads. Using experimental data from six patients with instrumented total knee arthroplasty (TKA), this study customized a lower limb musculoskeletal model to consider the positioning and the geometry of the implant at knee level. Static optimization was performed to estimate tibiofemoral contact forces and contact moments as well as musculotendinous forces. Predictions from both a generic and a customized model were compared to the instrumented implant measurements. Both models accurately predict superior-inferior (SI) force and abduction-adduction (AA) moment. Notably, the customization improves prediction of medial-lateral (ML) force and flexion-extension (FE) moments. However, there is subject-dependent variability in the prediction of anterior-posterior (AP) force. The customized models presented here predict loads on all joint axes and in most cases improve prediction. Unexpectedly, this improvement was more limited for patients with more rotated implants, suggesting a need for further model adaptations such as muscle wrapping or redefinition of hip and ankle joint centers and axes.

A multi-asperity adhesive contact model for catheter and vascular artery contact in endovascular surgery.

Contact behaviors of medical devices, such as guidewires and catheters, are critical in endovascular surgeries. In this work, a new method to predict adhesive contact force between catheter and vascular artery is presented. Multi-asperity adhesion on the surface of vascular artery, deformation of asperity and deformation of vascular substrate are all considered. The single asperity behavior is described with Johnson-Kendall-Roberts (JKR) contact model. The multi-asperity behavior is based on Greenwood-Williamson (GW) asperity model. Vascular substrate is considered as elastic bulk substrate and its deformation is determined with Hertzian pressure from asperity on a circular region on the elastic half space. The model shows that the deformation of vascular substrate accounts for the majority of the total contact deformation and significantly affects the predicted contact force. The model is verified with published experimental data. The comparison shows that the model produces very accurate prediction of contact force between catheter and vascular artery when the contact force is compressive. Parametric analysis based on asperity topography is carried out. The analysis shows that the diameter of the circular region of the interface between asperity and vascular substrate has more significant effect on the estimation of contact force than the radius of asperity. Further validation of prediction accuracy of the model under experiment is needed.

EMG-Driven Musculoskeletal Model Calibration With Wrapping Surface Personalization.

Muscle forces and joint moments estimated by electromyography (EMG)-driven musculoskeletal models are sensitive to the wrapping surface geometry defining muscle-tendon lengths and moment arms. Despite this sensitivity, wrapping surface properties are typically not personalized to subject movement data. This study developed a novel method for personalizing OpenSim cylindrical wrapping surfaces during EMG-driven model calibration. To avoid the high computational cost of repeated OpenSim muscle analyses, the method uses two-level polynomial surrogate models. Outer-level models specify time-varying muscle-tendon lengths and moment arms as functions of joint angles, while inner-level models specify time-invariant outer-level polynomial coefficients as functions of wrapping surface parameters. To evaluate the method, we used walking data collected from two individuals post-stroke and performed four variations of EMG-driven lower extremity model calibration: 1) no calibration of scaled generic wrapping surfaces (NGA), 2) calibration of outer-level polynomial coefficients for all muscles (SGA), 3) calibration of outer-level polynomial coefficients only for muscles with wrapping surfaces (LSGA), and 4) calibration of cylindrical wrapping surface parameters for muscles with wrapping surfaces (PGA). On average compared to NGA, SGA reduced lower extremity joint moment matching errors by 31%, LSGA by 24%, and PGA by 12%, with the largest reductions occurring at the hip. Furthermore, PGA reduced peak hip joint contact force by 47% bodyweight, which was the most consistent with published in vivo measurements. The proposed method for EMG-driven model calibration with wrapping surface personalization produces physically realistic OpenSim models that reduce joint moment matching errors while improving prediction of hip joint contact force.

Prediction of Model Generated Patellofemoral Joint Contact Forces Using Principal Component Prediction and Reconstruction.

It is not currently possible to directly and noninvasively measure in vivo patellofemoral joint contact force during dynamic movement; therefore, indirect methods are required. Simple models may be inaccurate because patellofemoral contact forces vary for the same knee flexion angle, and the patellofemoral joint has substantial out-of-plane motion. More sophisticated models use 3-dimensional kinematics and kinetics coupled to a subject-specific anatomical model to predict contact forces; however, these models are time consuming and expensive. We applied a principal component analysis prediction and regression method to predict patellofemoral joint contact forces derived from a robust musculoskeletal model using exclusively optical motion capture kinematics (external approach), and with both patellofemoral and optical motion capture kinematics (internal approach). We tested this on a heterogeneous population of asymptomatic subjects (n = 8) during ground-level walking (n = 12). We developed equations that successfully capture subject-specific gait characteristics with the internal approach outperforming the external. These approaches were compared with a knee-flexion based model in literature (Brechter model). Both outperformed the Brechter model in interquartile range, limits of agreement, and the coefficient of determination. The equations generated by these approaches are less computationally demanding than a musculoskeletal model and may act as an effective tool in future rapid gait analysis and biofeedback applications.

Prediction of in vivo hip contact forces during common activities of daily living using a segment-based musculoskeletal model.

Background: Quantifying in vivo hip muscle and contact forces during activities of daily living (ADL) provides valuable information for diagnosis and treatment of hip-related disorders. The objective of this study was to utilize Freebody, a segment-based musculoskeletal model, for the prediction of hip contact forces using a novel objective function during seven common ADLs and validate its performance against the publicly available HIP98 dataset. Methods: Marker data, ground reaction forces, and hip contact forces during slow, normal, and fast walking, stair ascent and descent, and standing up and sitting down were extracted for 3 subjects from the HIP98 dataset. A musculoskeletal anatomical dataset was scaled to match the dimensions of each subject, and muscle and hip contact forces were estimated by minimizing a novel objective function, which was the summation of the muscle stresses squared and body weight-normalised hip contact force. The accuracy of predictions were quantified using several metrics, and muscle forces were qualitatively compared to experimental EMGs in the literature. Results: FreeBody predicted the hip contact forces during the ADLs with encouraging accuracy: The root mean squared error of predictions were 44.0 ± 8.5, 47.4 ± 6.5, and 59.8 ± 7.1% BW during slow, normal, and fast walking, 44.2 ± 16.8% and 53.3 ± 12.2% BW for stair ascent and descent, and 31.8 ± 8.2% and 17.1 ± 5.0% BW for standing up and sitting down, respectively. The error in prediction of peak hip contact forces were 14-18%, 24-28%, 17-35% for slow, normal, and fast walking, 7-25% and 15-32% in stair ascent and descent, and around 10% for standing up and sitting down. The coefficient of determination was larger than 0.90 in all activities except in standing up (0.86 ± 0.08). Conclusion: This study has implemented a novel objective function in a segment-based musculoskeletal model, FreeBody, for the prediction of hip contact forces during a large range of ADLs. The model outputs compare favourably for all ADLs and are the best in standing up and sitting down, while muscle activation patterns are consistent with experimental EMGs from literature. This new objective function addresses one of the major limitations associated with musculoskeletal models in the literature, namely the high non-physiological predicted hip joint contact forces.

Alterations in the Functional Knee Alignment Are Not an Effective Strategy to Modify the Mediolateral Distribution of Knee Forces During Closed Kinetic Chain Exercises.

Pain felt while performing rehabilitation exercises could be a reason for the low adherence of knee osteoarthritis patients to physical rehabilitation. Reducing compressive forces on the most affected knee regions may help to mitigate the pain. Knee frontal plane positioning with respect to pelvis and foot (functional knee alignment) has been shown to modify the mediolateral distribution of the tibiofemoral joint contact force in walking. Hence, different functional knee alignments could be potentially used to modify joint loading during rehabilitation exercises. The aim was to understand whether utilizing different alignments is an effective strategy to unload specific knee areas while performing rehabilitation exercises. Eight healthy volunteers performed 5 exercises with neutral, medial, and lateral knee alignment. A musculoskeletal model was modified for improved prediction of tibiofemoral contact forces and used to evaluate knee joint kinematics, moments, and contact forces. Functional knee alignment had only a small and inconsistent effect on the mediolateral distribution joint contact force. Moreover, the magnitude of tibiofemoral and patellofemoral contact forces, knee moments, and measured muscle activities was not significantly affected by the alignment. Our results suggest that altering the functional knee alignment is not an effective strategy to unload specific knee regions in physical rehabilitation.

An Extended Hertz Model for Incompressible Mooney–Rivlin Half-Space Under Finite Spherical Indentation

In this paper, an extended Hertz model is proposed to predict the mechanical responses of the incompressible Mooney–Rivlin half-space under finite spherical indentation. The contact behaviors are systematically investigated through the axisymmetric finite element (FE) model using various constitutive parameter ratios. Based on the numerical results, the radius of contact zone is first derived by adopting the exact function of the indenter shape. A reliable prediction of contact force, for the incompressible neo-Hookean model, is then obtained by substituting newly modified contact radius into the original Hertz model. From this base, a correction factor, which characterizes the effect of constitutive parameter ratio, is introduced to predict the contact force, and the contact pressure distribution of the original Hertz’ formula is also extended for the incompressible Mooney–Rivlin half-space under spherical indentation up to the indenter radius. The extended Hertz model related to constitutive parameters is validated effectively through FE simulations and experimental indentation results under finite spherical indentation. On the contrary, when the indentation depth exceeds 10% of the indenter radius, the original Hertz model is unable to predict the strong effect of constitutive parameter ratio on the contact behaviors for the incompressible Mooney–Rivlin half-space.

Analysis of a cross groove constant velocity joint mechanism designed for high performance racing conditions

Constant Velocity Joint (CVJ) mechanisms enable torque transmission between two shafts at a fixed or variable angle. Cross groove CVJs are typically used in high performance automotive applications due to their versatility and light weight. Critical failure modes, such as pitting or abrasive wear, occur due to the harsh tribological conditions at ball reversals. In this research, an existing mathematical model is developed further for the case of cross groove CVJs including an accurate contact mechanics model. The developed model is validated against a published data set from literature. Surface topography of worn raceways are experimentally measured and the results from the developed model are corroborated with the measured surface parameters. This improved model shows the correlations between predicted contact force variation and wear scar depths during ball reversals, hitherto not reported in the literature.

Prediction of wheel-rail contact forces using simple onboard monitoring system and machine learning

For safe railway operation, periodic measuring of vehicle dynamics (wheel-rail-contact forces) is important, especially for tilting trains since they run faster through curves than normal traffic. So far, these forces are determined in test runs once a year using instrumented wheelsets. To get information more regularly and more economically, a simple onboard monitoring system for daily use on a commercial train has been developed. This system is predicting the forces relevant to assess running safety of tilting trains, so it is optimised for curves with lateral forces close to the critical values. Vertical forces are predicted by metering the primary spring deflection, which is already a proven method. The ambitious part research is focussing on is the prediction of the lateral forces on the whole wheelset and on the guiding wheel. This is obtained by transferring lateral accelerations using machine learning to manage even non-linear effects of the train’s undercarriage. Finally, the used Random Forest regressor thereby shows a good accuracy of the predicted forces compared to the original forces of the instrumented wheelset with correlations of around 95% for the relevant tilting train track sections.

A subject-specific musculoskeletal model to predict the tibiofemoral contact forces during daily living activities

Accurate prediction of tibiofemoral contact force (TFCF) during daily living activities is significant for understanding the initiation, progression, and treatment of knee osteoarthritis (KOA). However, the diversity of target activities, prediction accuracy, and computational efficiency of the current musculoskeletal simulations need to be further improved. In this study, a subject-specific musculoskeletal model considered the tibiofemoral alignment, medial-lateral contact locations, secondary tibiofemoral and all patellofemoral motions, and knee ligaments was proposed to predict the TFCFs during the five daily activities (normal walking, sit-to-stand, stand-to-sit, stair ascent, and stair descent) in OpenSim software. The standing lower-limbs-full-length radiograph, local radiograph of knee joint, motion capture data, and force plate data of eighteen subjects were acquired as the input data of the musculoskeletal model. The results showed good agreements of TFCFs between the predictions based on our proposed musculoskeletal model and the in-vivo measurements based on instrumented knee implants during the five daily activities (RMSE: 0.16 ∼ 0.31 BW, R2: 0.88 ∼ 0.97, M: −0.11 ∼ −0.02, P: 0.03 ∼ 0.10, and C: 0.04 ∼ 0.14). Additionally, the order of the peak total and lateral TFCFs from low to high was normal walking, stair ascent and stand-to-sit, and stair descent and sit-to-stand (P < 0.05), and the peak medial TFCF was stand-to-sit, sit-to-stand, normal walking, stair ascent and stair descent (P < 0.05). The outcomes of this study are valuable for further understanding the knee biomechanics during daily living activities and providing theoretical guidance for the treatments of KOA.